Shelf relevance: a revolutionary approach to assortment planning and execution

Shelf relevance: a revolutionary approach to assortment planning and execution

May 12, 2022 | By HIVERY

Grocery shoppers continue confronting frustrating out-of-shelf situations. Gaps in On-Shelf Availability and On-Shelf Relevancy result in lost volume, lost baskets, and lost faith among shoppers. Food retailers and suppliers need to work together more effectively to ensure their shelves have the right depth and breadth of assortment plus inventory to meet the needs of their local customers. They are keeping shelves relevant and properly stocked—in stores and online. 

Shelf relevance and the right store clustering strategies are critical to success in the current cut-throat environment. Today Retailers, grocers, and brand marketers across the board are taking steps to streamline assortments and make the shelves ‘locally relevant’ and ‘effectively merchandised.’ Simply put: hyper-local retailing is gaining relevance and requires new thinking and solutions to achieve success.

According to a study by IHL Group, shelf relevance costs retailers nearly $US1.1 trillion globally. In other words, same-store sales could increase by 10.3% if ‘shelf relevance’ is taken care of. ‘Overstocking’ alone is costing the North American retail industry $US129 billion.

Retailers, grocers, and their suppliers need fundamental new thinking and approach to assortments on shelves. 

It’s a no-brainer that retailers who get the assortment right enjoy more sales, higher gross margins, leaner operations, and most importantly, more loyal customers. Assortment planning is rarely a simple process for retailers. Currently, getting the right assortment and space strategy is an extremely long and ineffective process—it is based on data analyzed on an ‘averages of averages’ and some gut feel. Instead of using granular data and advanced analytics to augment and improve decision making, grocery retailers too often rely on aggregate and disconnected data internally and from external suppliers to make decisions about what to put on the shelf, how much of that UPC/SKU to stock, and how to price the items. Such an approach takes weeks if not months and quickly loses fidelity with your business strategy as you strain to operationalize these insights. Retailers, grocers, and their suppliers need fundamental new thinking and approach. 

“As the volume, variety, and velocity of datasets grow and the complexity of merchandising rules and constraints become diverse, traditional methods and solutions designed decades ago simply cannot handle such problems. It requires a new way of thinking, a new approach,” says Dirk Herdes, Vice President of Retail, HIVERY.

“The answer lies in AI plus human thinking,” exclaims Herdes. Unlike traditional “merchandise analytics’ solutions, AI puts the power back in the hands of the merchandising and operational teams that deeply understand the business and need to drive execution. With AI-fueled tools, Merchandising leadership and their cross-functional teams can rapidly conduct cluster and assortment strategy simulations to quickly inform the best actions to take with an accelerated path to execution. 

Herdes continued,  “With sophisticated machine learning algorithms, it drives merchandising decisions to a level of precision not possible with current offerings. It essentially combines the tasks of assortment analysis, category assessment, assortment optimization, and planogram development into one solution, so that teams can focus on what humans are good at; being more strategic, targeted, and transparent in their assortment decisions”. See the video below that explains how we have used AI in this space:

Using machine-learning approaches, HIVERY’s flagship product Curate seeks to use hyper-localized product and space recommendations aligned with your business objectives and operational realities to generate actionable insight. It takes into consideration the various merchandising rules and constraints in order to provide relevant, effective, and executable merchandising decisions. Using granular store-item level data, HIVERY Curate enables retailers and their supplier partners to quickly simulate assortment strategies, then fine-tune those simulations and generate assortment and space-aware planograms for execution. HIVERY Curate's AI-driven, analytics-based assortment optimization is simple to use, delivers massive time savings, and fuels transparent data-driven collaboration for merchants, category managers, and sales and operations teams.

“HIVERY gives grocery retailers a whole new way to execute at the level of scale, speed, and precision required in today’s market,” said Herdes

Being an AI-fueled platform, HIVERY helps grocers take advantage of the speed empowering them to make faster decisions and quickly turn those into execution. Given that grocers have limited capital resources and are required to ensure every decision provides a return on investment, HIVERY helps them gain precision in order to be really targeted in their execution and understand the impact of each decision before taking action. HIVERY also fosters transparency while grocers collaborate with suppliers so that all parties understand the "why" of the decision made to keep the focus on their joint customers.  

With HIVERY, for the first time ever, retailers and their merchandising teams have AI or a complete “data science department” essentially in their pocket and can run unlimited assortment and space strategy simulations, finding the right answers and executing that strategy rapidly.  In a matter of  minutes, retailers can find answers to strategic questions like:

  • What is the value of going store-specific? 
  • What is the opportunity to re-optimize the existing clusters strategy? 
  • Where is the breakeven point for my clustering strategy to balance revenue gains and operational resources?
  • How can I optimize my days of supply strategy to reduce out-of-stocks and meet the local demands of each store?  

For years grocery retailers have been working with "averages of averages" using traditional methods and practices. The lack of relevant tools in the market only added to their inability to conduct "bottom-up" analyses. A bottom-up approach to assortment and clusterin actually identifies new shopper segments, ones not based on traditional methods like “demographics”. It is a fundamentally more granular approach and therefore identifies different shopper segments and their associated preferences for products in stores, and hence their own elasticity of demand. This impacts economic concepts such as “demand transference”. Using AI with bottoms-up datasets allows retailers and their supplier partners to understand the impact of adding new SKU/UPCs or removing existing products to the category space, all at the individual store level.  

HIVERY utilizes store-item level data and looks at shoppers' purchase decisions at the shelf level. Uncovering hidden insights about shoppers in that specific store and starting to unlock new growth opportunities is not possible today. HIVERY Curate’s approach considers broad demographics and brand preferences by observing shopper behavior at this granular level and ensuring your recommendations are able to be executed and actioned.  Enabling teams to simulate various assortment and clustering strategies that are seamlessly pushed down to the level of retail execution. The unique part is this:  HIVERY Curate can consider the retailers' goals for each category along with considering (i.e., grow revenue and/or volume) any merchandising rules or constraints. Doing all of this in minutes not months. “With the use of AI models in HIVERY Curate, you don't have to think about analytics and execution as two separate things. AI allows you to look at them together and really accelerate how you operationalize those insights,” says Herdes.

Changing with way retailers and suppliers collaborate

"We are literally democratizing AI in the hands of operators and decision-makers,” notes Herdes. AI models used by HIVERY allow its clients to augment the existing process and data while managing complexity. Leveraging AI, HIVERY has made it possible for retailers to achieve locally relevant planograms. What was time-consuming and complicated to achieve, has been made possible.  HIVERY is fundamentally changing the way retailers and their supplier partners can collaborate with regard to assortment and space decisions. Data indeed has a better idea and is making hyper-local a reality!

Related resources you might be interested in:

Webinar: Leveraging AI towards locally relevant shelves: Why data has a better idea about your store clusters

Guide: Retail Store Cluster Strategies: Leveraging AI Towards Locally Relevant Shelves

Insight: AI, Assortment Planning, and Supply Chain: What to Know

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